Prediction and multi‐objective optimization of tidal current turbines considering cavitation based on GA‐ANN methods

Abstract

As a new type of energy, tidal current energy is increasingly gaining attention worldwide. However, the tidal current energy development field faces a key technical problem of how to improve the generation efficiency and service life of tidal current generators. In this study, an innovative optimized design method of the horizontal axis tidal turbine was applied to the rotor blade design. To be specific, blade cavitation was considered as a factor for optimization and then was improved using a modern optimization method that combined artificial neural networks and genetic algorithms, solving for a numerical solution. In the optimization program, the annual energy output and the superficial area of the rotor blade served as the optimization goals, and the chord length and the twist angle served as the optimization variables. The cavitation constraint was different from the traditional method in that the judgment criterion of occurrence of cavitation was no longer the minimum pressure coefficient but the maximum stress coefficient. The combination of the artificial neural network and the genetic algorithm not only realized high optimization accuracy but also greatly saved computation time. To verify the validity of the optimization method, a 1 MW turbine was designed, and the optimal solution was chosen from the formed Pareto optimal solution set for modeling, and the new model was compared with the model quoted in the reference. The simulation result shows that the turbine designed using the optimization method not only reduces the surface area of the rotor blade and improves the annual energy output, but also remarkably improves the blades' cavitation resistance. The optimization analysis and comparison using different cavitation judgment criteria shows that the cavitation resistance of blades obtained using the maximum stress coefficient as the judgment criterion is more advantageous.

A Study on Diffuser Augmentation of a Tidal Turbine

“…The ANN algorithm is a mathematical algorithm model for the processing of distributed parallel information, which imitates the behavior of animal neural networks. In the optimization system, the performance parameters of the temporary turbine are measured by ANN [10]. GA is a method to resolve optimization problems inspired by the natural selection process and to produce potential solutions from all possible solutions.…”

Research on the steady‐state operation optimization technology of oil pipeline

“…At present, computer technology has been applied in many fields to obtain optimal solutions, such as microgrid, solar energy, and renewable energy. [24][25][26][27][28][29][30][31][32] Many scholars have also proposed T A B L E 1 Improvement of process equipment or technology of the oil pipeline system Frequency control technology The output power of the motor is effectively matched with the pipe flow, which reduces energy consumption…”

“…With the continuous development of mathematics and computer science, the continuous updating of artificial intelligence technology and intelligent optimization algorithms makes the process of solving many engineering problems simple, and the solution is better. At present, computer technology has been applied in many fields to obtain optimal solutions, such as microgrid, solar energy, and renewable energy 24‐32 . Many scholars have also proposed different solutions in the hot oil pipeline system, as shown in Table 2.…”